Load Libraries
require(tidyverse)
## Loading required package: tidyverse
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.5 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
require(lubridate)
## Loading required package: lubridate
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## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union
require(stringr)
require(readxl)
## Loading required package: readxl
require(arsenal)
## Loading required package: arsenal
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## Attaching package: 'arsenal'
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## is.Date
require(gtsummary)
## Loading required package: gtsummary
require(plotly)
## Loading required package: plotly
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## Attaching package: 'plotly'
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## last_plot
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Load From file
needs <- read.csv("../Data/Cleaned/01_Needs.csv")
names(needs) <- toupper(names(needs))
referrals <- read.csv("../Data/Cleaned/01_referrals.csv")
names(referrals) <- toupper(names(referrals))
Type Covert
needs <- needs %>% mutate(AGE = as.factor(AGE), Gender = as.factor(GENDER), LANGUAGE = as.factor(LANGUAGE)
,MILITARY_STATUS = as.factor(MILITARY_STATUS)
,DISABILITY_STATUS = as.factor(DISABILITY_STATUS)
,DISABILITY_TYPE = as.factor(DISABILITY_TYPE)
,HEALTH_INSURANCE = as.factor(HEALTH_INSURANCE)
);
referrals <- referrals %>% mutate(AGE = as.factor(AGE), Gender = as.factor(GENDER), Language = as.factor(LANGUAGE)
,MILITARY_STATUS = as.factor(MILITARY_STATUS)
,DISABILITY_STATUS = as.factor(DISABILITY_STATUS)
,DISABILITY_TYPE = as.factor(DISABILITY_TYPE)
,HEALTH_INSURANCE = as.factor(HEALTH_INSURANCE))
Generate Stats
needs %>% select(NEED) %>% tbl_summary()
| Characteristic | N = 26,0951 |
|---|---|
| NEED | |
| Arts Culture and Recreation | 2 (<0.1%) |
| Clothing/Personal/Household Needs | 112 (0.4%) |
| Disaster Services | 181 (0.7%) |
| Education | 10 (<0.1%) |
| Employment | 241 (0.9%) |
| Food/Meals | 737 (2.8%) |
| Health Care | 285 (1.1%) |
| Housing | 10,915 (42%) |
| Income Support/Assistance | 285 (1.1%) |
| Individual Family and Community Support | 285 (1.1%) |
| Information Services | 11,931 (46%) |
| Legal Consumer and Public Safety Services | 283 (1.1%) |
| Mental Health/Addictions | 493 (1.9%) |
| Other Government/Economic Services | 39 (0.1%) |
| Transportation | 123 (0.5%) |
| Utility Assistance | 77 (0.3%) |
| Volunteers/Donations | 96 (0.4%) |
|
1
n (%)
|
|
NEED : Replace non housing and non information services with others
needs <- needs %>% mutate(NEED = if_else(NEED != 'Housing' & NEED != 'Information Services','Others',NEED))
needs$NEED <- as.factor(needs$NEED)
Generate Stats
needs %>% select(CE_SCREENED) %>% tbl_summary()
| Characteristic | N = 26,0951 |
|---|---|
| CE_SCREENED | |
| DV Referral | 920 (3.5%) |
| Literally Homeless | 19,621 (75%) |
| Precariously Housed | 359 (1.4%) |
| Risk of Homelessness | 5,056 (19%) |
| ROI Declined | 100 (0.4%) |
| Unknown | 39 |
|
1
n (%)
|
|
needs %>% select(NEED,CE_SCREENED) %>% tbl_summary(by = NEED )
| Characteristic | Housing, N = 10,9151 | Information Services, N = 11,9311 | Others, N = 3,2491 |
|---|---|---|---|
| CE_SCREENED | |||
| DV Referral | 581 (5.3%) | 85 (0.7%) | 254 (7.8%) |
| Literally Homeless | 8,077 (74%) | 9,304 (78%) | 2,240 (69%) |
| Precariously Housed | 124 (1.1%) | 178 (1.5%) | 57 (1.8%) |
| Risk of Homelessness | 2,064 (19%) | 2,328 (20%) | 664 (20%) |
| ROI Declined | 48 (0.4%) | 24 (0.2%) | 28 (0.9%) |
| Unknown | 21 | 12 | 6 |
|
1
n (%)
|
|||
needs %>% select(NEED = NEED,NEED_1 = NEED) %>% tbl_summary(by = NEED)
| Characteristic | Housing, N = 10,9151 | Information Services, N = 11,9311 | Others, N = 3,2491 |
|---|---|---|---|
| NEED_1 | |||
| Housing | 10,915 (100%) | 0 (0%) | 0 (0%) |
| Information Services | 0 (0%) | 11,931 (100%) | 0 (0%) |
| Others | 0 (0%) | 0 (0%) | 3,249 (100%) |
|
1
n (%)
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|||
Q1 —-
tab1 <- tableby(~NEED,data = needs %>% select(NEED,CONTACT_NUMBER) %>% distinct())
q1.a <- as.data.frame(tab1)
summary(tab1)
##
##
## | | Overall (N=16696) |
## |:--------------------------------------|:-----------------:|
## |**NEED** | |
## | Housing | 6946 (41.6%) |
## | Information Services | 8840 (52.9%) |
## | Others | 910 (5.5%) |
needs %>% select(NEED,CONTACT_NUMBER) %>% distinct() %>% select(NEED) %>% tbl_summary()
| Characteristic | N = 16,6961 |
|---|---|
| NEED | |
| Housing | 6,946 (42%) |
| Information Services | 8,840 (53%) |
| Others | 910 (5.5%) |
|
1
n (%)
|
|
q1.a.plt <- needs %>%select(NEED,CONTACT_NUMBER) %>% distinct() %>% group_by(NEED) %>% summarise(count=n())
fig <- plot_ly(data = q1.a.plt,x=~NEED,y=~count,type = 'bar')
fig <- fig %>% layout(yaxis = list(title = 'Count'), barmode = 'group')
fig
q1.b <- needs %>% select(CONTACT_NUMBER,NEED) %>% distinct() %>% group_by(CONTACT_NUMBER) %>% summarise(NUMBER_OF_NEEDS = n())
q1.b_2 <- q1.b %>% group_by(NUMBER_OF_NEEDS) %>% summarise(NUMBER_OF_CLIENTS = n())
q1.b_2
## # A tibble: 3 x 2
## NUMBER_OF_NEEDS NUMBER_OF_CLIENTS
## <int> <int>
## 1 1 12666
## 2 2 1790
## 3 3 150
q1.c <- needs %>% filter(NEED == 'Housing' | NEED == 'Information Services') %>% distinct(CONTACT_NUMBER,CE_SCREENED,NEED)
q1.c %>% select(CE_SCREENED,NEED) %>% tbl_summary(by = CE_SCREENED,missing = "no") %>% modify_table_body(filter,label %in% c('Housing','Information Services'))
## 17 observations missing `CE_SCREENED` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `CE_SCREENED` column before passing to `tbl_summary()`.
| Characteristic | DV Referral, N = 4101 | Literally Homeless, N = 12,7341 | Precariously Housed, N = 1771 | Risk of Homelessness, N = 2,6441 | ROI Declined, N = 481 |
|---|---|---|---|---|---|
| Housing | 366 (89%) | 5,517 (43%) | 62 (35%) | 1,112 (42%) | 32 (67%) |
| Information Services | 44 (11%) | 7,217 (57%) | 115 (65%) | 1,532 (58%) | 16 (33%) |
|
1
n (%)
|
|||||
q1.d <- needs %>% filter(!(CONTACT_NUMBER %in% q1.c$CONTACT_NUMBER)) %>% distinct(CONTACT_NUMBER,CE_SCREENED,NEED)
q1.d %>% select(CE_SCREENED,NEED) %>% tbl_summary(by = CE_SCREENED,missing = "no") %>% modify_table_body(filter,!(label %in% c('Housing','Information Services')))
## 3 observations missing `CE_SCREENED` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `CE_SCREENED` column before passing to `tbl_summary()`.
| Characteristic | DV Referral, N = 241 | Literally Homeless, N = 1401 | Precariously Housed, N = 21 | Risk of Homelessness, N = 261 | ROI Declined, N = 21 |
|---|---|---|---|---|---|
| NEED | |||||
| Others | 24 (100%) | 140 (100%) | 2 (100%) | 26 (100%) | 2 (100%) |
|
1
n (%)
|
|||||
q1.e <- needs %>% filter(NEED == 'Housing') %>% select(CONTACT_NUMBER, CE_SCREENED, GENDER) %>% distinct()
q1.e %>% select(CE_SCREENED,GENDER) %>% tbl_summary(by = GENDER,missing = "no")
## 131 observations missing `GENDER` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `GENDER` column before passing to `tbl_summary()`.
| Characteristic | Female, N = 4,3341 | Male, N = 2,6061 | Refused to Disclose, N = 71 | Transgender, N = 231 |
|---|---|---|---|---|
| CE_SCREENED | ||||
| DV Referral | 307 (7.1%) | 39 (1.5%) | 1 (14%) | 0 (0%) |
| Literally Homeless | 3,148 (73%) | 2,273 (87%) | 5 (71%) | 17 (74%) |
| Precariously Housed | 46 (1.1%) | 10 (0.4%) | 0 (0%) | 0 (0%) |
| Risk of Homelessness | 804 (19%) | 274 (11%) | 1 (14%) | 6 (26%) |
| ROI Declined | 21 (0.5%) | 9 (0.3%) | 0 (0%) | 0 (0%) |
|
1
n (%)
|
||||
#tbl_by <- tableby(GENDER ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Housing'))
#summary(tbl_by)
#q1.e <- as.data.frame(tbl_by)
q1.f <- needs %>% filter(NEED == 'Information Services') %>% select(CONTACT_NUMBER, CE_SCREENED, GENDER) %>% distinct()
q1.f %>% select(CE_SCREENED,GENDER) %>% tbl_summary(by = GENDER,missing = "no")
## 190 observations missing `GENDER` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `GENDER` column before passing to `tbl_summary()`.
| Characteristic | Female, N = 4,9081 | Male, N = 3,7821 | Refused to Disclose, N = 181 | Transgender, N = 311 |
|---|---|---|---|---|
| CE_SCREENED | ||||
| DV Referral | 35 (0.7%) | 5 (0.1%) | 0 (0%) | 0 (0%) |
| Literally Homeless | 3,706 (76%) | 3,326 (88%) | 11 (61%) | 26 (84%) |
| Precariously Housed | 70 (1.4%) | 27 (0.7%) | 0 (0%) | 1 (3.2%) |
| Risk of Homelessness | 1,088 (22%) | 415 (11%) | 7 (39%) | 4 (13%) |
| ROI Declined | 7 (0.1%) | 8 (0.2%) | 0 (0%) | 0 (0%) |
|
1
n (%)
|
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#tbl_by <- tableby(GENDER ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Information Services'))
#summary(tbl_by)
#q1.f <- as.data.frame(tbl_by)
q1.g <- needs %>% filter(NEED == 'Housing') %>% select(CONTACT_NUMBER, DISABILITY_STATUS, CE_SCREENED) %>% distinct()
q1.g %>% select(DISABILITY_STATUS,CE_SCREENED) %>% tbl_summary(by = DISABILITY_STATUS,missing = "no")
## 156 observations missing `DISABILITY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `DISABILITY_STATUS` column before passing to `tbl_summary()`.
| Characteristic | No, N = 4,7021 | Not Collected, N = 661 | Refused to Disclose, N = 171 | Yes, N = 2,1601 |
|---|---|---|---|---|
| CE_SCREENED | ||||
| DV Referral | 243 (5.2%) | 6 (9.1%) | 6 (35%) | 78 (3.6%) |
| Literally Homeless | 3,614 (77%) | 53 (80%) | 10 (59%) | 1,759 (81%) |
| Precariously Housed | 38 (0.8%) | 0 (0%) | 0 (0%) | 16 (0.7%) |
| Risk of Homelessness | 781 (17%) | 6 (9.1%) | 0 (0%) | 295 (14%) |
| ROI Declined | 17 (0.4%) | 1 (1.5%) | 1 (5.9%) | 11 (0.5%) |
|
1
n (%)
|
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#tbl_by <- tableby(DISABILITY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Housing'))
#summary(tbl_by)
#q1.g <- as.data.frame(tbl_by)
q1.h <- needs %>% filter(NEED == 'Information Services') %>% select(CONTACT_NUMBER, DISABILITY_STATUS, CE_SCREENED) %>% distinct()
q1.h %>% select(DISABILITY_STATUS,CE_SCREENED) %>% tbl_summary(by = DISABILITY_STATUS,missing = "no")
## 196 observations missing `DISABILITY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `DISABILITY_STATUS` column before passing to `tbl_summary()`.
| Characteristic | No, N = 5,9181 | Not Collected, N = 3711 | Refused to Disclose, N = 241 | Yes, N = 2,4201 |
|---|---|---|---|---|
| CE_SCREENED | ||||
| DV Referral | 29 (0.5%) | 0 (0%) | 0 (0%) | 10 (0.4%) |
| Literally Homeless | 4,719 (80%) | 357 (96%) | 22 (92%) | 1,967 (81%) |
| Precariously Housed | 71 (1.2%) | 2 (0.5%) | 0 (0%) | 23 (1.0%) |
| Risk of Homelessness | 1,086 (18%) | 11 (3.0%) | 1 (4.2%) | 416 (17%) |
| ROI Declined | 10 (0.2%) | 1 (0.3%) | 1 (4.2%) | 3 (0.1%) |
|
1
n (%)
|
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#tbl_by <- tableby(DISABILITY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Information Services'))
##summary(tbl_by)
#q1.h <- as.data.frame(tbl_by)
q1.i <- needs %>% filter(NEED == 'Housing') %>% select(CONTACT_NUMBER, MILITARY_STATUS, CE_SCREENED) %>% distinct()
q1.i %>% select(MILITARY_STATUS,CE_SCREENED) %>% tbl_summary(by = MILITARY_STATUS,missing = "no")
## 207 observations missing `MILITARY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `MILITARY_STATUS` column before passing to `tbl_summary()`.
| Characteristic | Active, N = 31 | No, N = 6,5011 | Not Collected, N = 331 | Refused to Disclose, N = 131 | Veteran, N = 3441 |
|---|---|---|---|---|---|
| CE_SCREENED | |||||
| DV Referral | 1 (33%) | 308 (4.7%) | 3 (9.1%) | 5 (38%) | 9 (2.6%) |
| Literally Homeless | 2 (67%) | 5,072 (78%) | 24 (73%) | 8 (62%) | 288 (84%) |
| Precariously Housed | 0 (0%) | 50 (0.8%) | 2 (6.1%) | 0 (0%) | 2 (0.6%) |
| Risk of Homelessness | 0 (0%) | 1,032 (16%) | 3 (9.1%) | 0 (0%) | 45 (13%) |
| ROI Declined | 0 (0%) | 29 (0.4%) | 1 (3.0%) | 0 (0%) | 0 (0%) |
|
1
n (%)
|
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#tbl_by <- tableby(MILITARY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Housing'))
##summary(tbl_by)
#q1.i <- as.data.frame(tbl_by)
q1.j <- needs %>% filter(NEED == 'Information Services') %>% select(CONTACT_NUMBER, MILITARY_STATUS, CE_SCREENED) %>% distinct()
q1.j %>% select(MILITARY_STATUS,CE_SCREENED) %>% tbl_summary(by = MILITARY_STATUS,missing = "no")
## 317 observations missing `MILITARY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `MILITARY_STATUS` column before passing to `tbl_summary()`.
| Characteristic | Active, N = 141 | No, N = 8,0921 | Not Collected, N = 251 | Refused to Disclose, N = 121 | Veteran, N = 4691 |
|---|---|---|---|---|---|
| CE_SCREENED | |||||
| DV Referral | 0 (0%) | 36 (0.4%) | 0 (0%) | 0 (0%) | 3 (0.6%) |
| Literally Homeless | 11 (79%) | 6,514 (81%) | 14 (56%) | 8 (67%) | 407 (87%) |
| Precariously Housed | 1 (7.1%) | 87 (1.1%) | 2 (8.0%) | 0 (0%) | 3 (0.6%) |
| Risk of Homelessness | 2 (14%) | 1,439 (18%) | 8 (32%) | 3 (25%) | 56 (12%) |
| ROI Declined | 0 (0%) | 13 (0.2%) | 1 (4.0%) | 1 (8.3%) | 0 (0%) |
|
1
n (%)
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#tbl_by <- tableby(MILITARY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Information Services'))
##summary(tbl_by)
#q1.j <- as.data.frame(tbl_by)
Q2 -
q2 <- needs %>% select(CONTACT_NUMBER,GENDER,DISABILITY_STATUS,MILITARY_STATUS,AGE,NEED,REFERRAL) %>% distinct()
q2 %>% select(-c(CONTACT_NUMBER)) %>% tbl_summary(by = REFERRAL,missing = "no")
## 113 observations missing `REFERRAL` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `REFERRAL` column before passing to `tbl_summary()`.
| Characteristic | 2020 Census, N = 31 | 211.org, N = 21 | 411 Directory Assistance, N = 41 | AARP, N = 21 | Agape Christian Counseling, N = 21 | American Cancer Society, N = 21 | American Red Cross - Charlotte Metro Chapter, N = 181 | Angel House Maternity Home, N = 111 | Area Agency on Aging - Centralina, N = 61 | Asheville-Buncombe Community Christian Ministry, N = 391 | Autism Society of North Carolina - Charlotte, N = 61 | Blue Haven, N = 11 | C.W. Williams Community Health Center, N = 171 | Cabarrus Victims Assistance Network, N = 31 | Camino Community Center, N = 31 | Campaign for Southern Equality, N = 31 | Caramore Community, N = 41 | Cardinal Innovations, N = 691 | Care Ring, N = 131 | Carolinas CARE Partnership, N = 91 | Carolinas Medical Center - Carolinas HealthCare System, N = 21 | Catherines House, N = 31 | Catholic Charities - Charlotte Regional Office, N = 201 | Center for Community Transitions, N = 51 | Center for Family Violence Prevention, N = 11 | Charles George Veterans Affairs Medical Center, N = 11 | Charlotte Area Fund, N = 1201 | Charlotte Area Transit System, N = 171 | Charlotte Berean Seventh Day Adventist Church Community Center, N = 441 | Charlotte Center for Legal Advocacy, N = 201 | Charlotte Community Health Clinic, N = 71 | Charlotte Family Housing, N = 2611 | Charlotte Rescue Mission, N = 91 | Charlotte Vet Center, N = 21 | Charlotte Works, N = 21 | Charlottetown Manor, N = 31 | Child Care Resources, N = 201 | Community Link, N = 5801 | Community Shelter of Union County, N = 51 | Community Support Services of Mecklenburg County, N = 4,4341 | Cooperative Christian Ministry, N = 61 | Coronavirus, N = 7011 | Crisis Assistance Ministry, N = 1451 | CriSyS, N = 21 | Davidson Housing Coalition, N = 31 | Debt Reduction Services, N = 21 | Department of Public Health - Mecklenburg County, N = 131 | Department of Social Services - Mecklenburg County, N = 1741 | Derita Presbyterian Church child care, N = 31 | Dilworth Soup Kitchen, N = 241 | Disability Rights and Resources, N = 41 | DreamKey Partners, N = 101 | Dress for Success - Charlotte, N = 111 | Durham Crisis Response Center, N = 11 | Easterseals UCP, N = 21 | EnergyUnited, N = 361 | Esther House, N = 21 | Family Promise of Gaston County, N = 51 | Family Promise of Wake County, N = 21 | FeedNC, N = 171 | Fifth Street Ministries, N = 71 | Florence Crittenton Services, N = 231 | Friendship Trays, N = 51 | Gaston Community Action, N = 11 | Goodwill - Southern Piedmont, N = 371 | Goodwill Career Connections Center - Catawba County Center, N = 61 | Government Services - City of Charlotte, N = 91 | Government Services - Mecklenburg County, N = 61 | Government Services - Scotland County, N = 21 | Grocery Worker's Relief Fund, N = 31 | Habitat for Humanity - Cabarrus County, N = 21 | Habitat for Humanity - Charlotte Region, N = 31 | Habitat for Humanity - Greater Matthews, N = 21 | Habitat for Humanity - Our Towns, N = 11 | Harvest Center of Charlotte, N = 1251 | Helping Hand Mission, N = 21 | HIV/AIDS Hotline, N = 21 | Homes of Hope, N = 21 | Hope Haven, N = 21 | Hope House Foundation, N = 1451 | Hope Street Food Pantry, N = 171 | Humane Society - Charlotte, N = 41 | If My People Food Pantry, N = 261 | Inlivian, N = 1561 | Internal Initiatives, N = 11 | Internal Revenue Service, N = 21 | Jewish Community Services of South Florida, N = 21 | Jewish Family Services of Greater Charlotte, N = 31 | Latin American Coalition, N = 31 | Leading Into New Communities, N = 141 | Legal Aid of North Carolina, N = 501 | Leukemia and Lymphoma Society, N = 21 | Liberty Baptist Church Food Pantry, N = 751 | Lifeline, N = 41 | LIFESPAN, N = 21 | Loaves and Fishes, N = 691 | Lois Lodge Maternity Home, N = 31 | Lutheran Services Carolinas, N = 11 | Matthews Free Medical Clinic, N = 41 | Matthews Help Center, N = 141 | Mayfield Memorial Apartments, N = 851 | McDowell Street Center for Family Law, N = 111 | McLeod Addictive Disease Center, N = 91 | Mecklenburg County Senior Center, N = 31 | MECKLINK Behavioral Healthcare, N = 21 | Mens Shelter of Charlotte, N = 3841 | Metrolina Association for the Blind, N = 11 | MiraVia, N = 171 | Mission Hospital, N = 21 | Mother of Mercy Catholic Church, N = 31 | Mount Olive Presbyterian Church, N = 501 | My Sisters Success of North Carolina, N = 11 | National Domestic Violence Hotline, N = 1611 | Navy-Marine Corps Relief Society, N = 31 | NC MedAssist, N = 151 | NCHousingSearch.org, N = 7111 | NCWorks Career Center - Mecklenburg County, N = 721 | NeedyMeds, N = 21 | New Outreach Christian Center, N = 331 | North Carolina Back@Home Program, N = 231 | North Carolina Baptist Aging Ministry, N = 51 | North Carolina Bar Association, N = 31 | North Carolina Continuum of Care, N = 11,3431 | North Carolina Council of the Blind, N = 51 | North Carolina Department of Commerce, N = 21 | North Carolina Division of Health Service Regulation, N = 41 | North Carolina Division of Motor Vehicles, N = 311 | North Carolina Division of Motor Vehicles - Mecklenburg County, N = 21 | North Carolina Division of Services for the Blind - Charlotte District, N = 31 | North Carolina Division of Vocational Rehabilitation - Charlotte, N = 61 | North Carolina Emergency Management, N = 21 | North Carolina Governor, N = 11 | North Carolina HOPE Program, N = 31 | North Carolina Housing Finance Agency, N = 21 | North Carolina Lions Club, N = 21 | North Carolina Marketplace In-Person Assistance, N = 21 | North Carolina Medicaid, N = 41 | North Carolina Missions of Mercy, N = 41 | North Carolina Operated Healthcare Facilities, N = 21 | North Carolina Oxford Houses, N = 51 | Novant Health - Michael Jordan Family Medical Clinic, N = 21 | Open Door Ministries of High Point, N = 11 | Operation Home Front, N = 41 | P. K. Management, N = 21 | Pinecrest Manor, N = 21 | Pines at Carolina Place The, N = 41 | Plaza Baptist Church Day Care, N = 31 | Pregnancy Resource Center of Charlotte, N = 21 | Presbyterian Healthcare, N = 51 | Primary Health-Care of Charlotte P.A., N = 21 | Professionals in Transition, N = 21 | Project Outpour, N = 61 | Prosperity Unlimited, N = 21 | Queen City Worship Center, N = 451 | RAIN, N = 71 | RAMP Charlotte, N = 1621 | Rape Abuse and Incest National Network, N = 31 | REAL Crisis Intervention, N = 31 | Rescue Missions Ministries, N = 11 | Resident Relief Foundation, N = 51 | Room at the Inn, N = 91 | Rowan Helping Ministries, N = 61 | Safe Alliance, N = 5941 | Safe Harbor, N = 1941 | Safe Haven of Person County, N = 21 | Salisbury Rowan Community Action Agency, N = 31 | Salvation Army - Cabarrus and Stanly Counties, N = 421 | Salvation Army - Greater Charlotte, N = 3701 | Salvation Army - Sandhills Region, N = 11 | Salvation Army - Wake County, N = 21 | Salvation Army Center of Hope - Gaston County, N = 251 | School District - Charlotte-Mecklenburg, N = 21 | Self-Help Credit Union - Charlotte, N = 51 | Selwyn Ave. Presbyterian Church Child Care, N = 31 | Servants Heart of Mint Hill, N = 81 | Shalom Adonai Church of God, N = 211 | Sharon Baptist Church Child Care, N = 31 | Sharon Village Retirement Apartments, N = 31 | SingleCare Prescription Card, N = 31 | St. Paul Baptist Church Food Pantry, N = 481 | Substance Abuse and Mental Health Services Administration, N = 21 | Supportive Housing Communities, N = 1631 | Syrenity House Empowered, N = 111 | The Relatives, N = 251 | Thrift United Methodist Church, N = 671 | Toys for Tots, N = 31 | Trans Lifeline, N = 31 | Turning Point, N = 451 | United States Centers for Disease Control and Prevention, N = 61 | United States Department of Veterans Affairs, N = 81 | United States Social Security Administration, N = 221 | United Way Association of South Carolina, N = 61 | United Way of North Carolina, N = 41 | University Soup Kitchen, N = 21 | Urban League of Central Carolinas, N = 111 | Urban Ministry Center, N = 3591 | Veterans Bridge Home, N = 421 | Victory Christian Center, N = 781 | YWCA - Central Carolinas, N = 721 |
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| GENDER | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Female | 3 (100%) | 2 (100%) | 2 (50%) | 2 (100%) | 0 (0%) | 2 (100%) | 11 (61%) | 9 (82%) | 3 (50%) | 12 (31%) | 6 (100%) | 1 (100%) | 6 (35%) | 3 (100%) | 3 (100%) | 0 (0%) | 2 (50%) | 39 (59%) | 9 (69%) | 4 (44%) | 0 (0%) | 3 (100%) | 14 (70%) | 2 (40%) | 1 (100%) | 1 (100%) | 56 (49%) | 14 (82%) | 28 (67%) | 10 (50%) | 5 (71%) | 221 (86%) | 2 (22%) | 0 (0%) | 2 (100%) | 0 (0%) | 18 (90%) | 383 (67%) | 3 (60%) | 2,484 (57%) | 6 (100%) | 391 (67%) | 82 (57%) | 2 (100%) | 1 (33%) | 2 (100%) | 3 (23%) | 104 (60%) | 3 (100%) | 11 (46%) | 2 (50%) | 8 (80%) | 11 (100%) | 1 (100%) | 0 (0%) | 23 (64%) | 2 (100%) | 5 (100%) | 2 (100%) | 8 (47%) | 7 (100%) | 21 (91%) | 2 (40%) | 0 (0%) | 13 (38%) | 6 (100%) | 9 (100%) | 3 (50%) | 0 (0%) | 0 (0%) | 2 (100%) | 1 (100%) | 0 (NA%) | 1 (100%) | 86 (69%) | 2 (100%) | 0 (0%) | 2 (100%) | 0 (0%) | 134 (96%) | 10 (59%) | 2 (50%) | 19 (79%) | 123 (80%) | 1 (100%) | 0 (0%) | 2 (100%) | 2 (67%) | 1 (33%) | 0 (0%) | 34 (68%) | 2 (100%) | 49 (67%) | 0 (0%) | 0 (0%) | 56 (81%) | 3 (100%) | 0 (0%) | 4 (100%) | 11 (79%) | 50 (62%) | 9 (82%) | 5 (56%) | 0 (0%) | 0 (0%) | 20 (5.3%) | 0 (0%) | 17 (100%) | 2 (100%) | 3 (100%) | 38 (79%) | 1 (100%) | 152 (94%) | 1 (33%) | 4 (31%) | 479 (69%) | 22 (32%) | 0 (0%) | 23 (70%) | 16 (70%) | 5 (100%) | 3 (100%) | 6,446 (58%) | 3 (60%) | 0 (0%) | 0 (0%) | 14 (45%) | 0 (0%) | 0 (0%) | 2 (33%) | 2 (100%) | 1 (100%) | 3 (100%) | 2 (100%) | 0 (0%) | 2 (100%) | 0 (0%) | 0 (0%) | 2 (100%) | 1 (20%) | 0 (0%) | 0 (0%) | 2 (50%) | 2 (100%) | 0 (0%) | 4 (100%) | 0 (0%) | 2 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (33%) | 2 (100%) | 26 (58%) | 0 (0%) | 134 (83%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (100%) | 7 (78%) | 2 (33%) | 522 (91%) | 186 (98%) | 2 (100%) | 0 (0%) | 31 (74%) | 344 (94%) | 0 (0%) | 2 (100%) | 19 (76%) | 2 (100%) | 5 (100%) | 3 (100%) | 8 (100%) | 14 (74%) | 3 (100%) | 0 (0%) | 0 (0%) | 31 (67%) | 2 (100%) | 99 (61%) | 11 (100%) | 7 (28%) | 46 (69%) | 3 (100%) | 0 (0%) | 45 (100%) | 6 (100%) | 5 (62%) | 12 (55%) | 6 (100%) | 2 (50%) | 0 (0%) | 5 (45%) | 154 (45%) | 10 (24%) | 52 (67%) | 69 (97%) |
| Male | 0 (0%) | 0 (0%) | 2 (50%) | 0 (0%) | 2 (100%) | 0 (0%) | 7 (39%) | 2 (18%) | 3 (50%) | 27 (69%) | 0 (0%) | 0 (0%) | 11 (65%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (50%) | 25 (38%) | 4 (31%) | 5 (56%) | 2 (100%) | 0 (0%) | 6 (30%) | 3 (60%) | 0 (0%) | 0 (0%) | 59 (51%) | 3 (18%) | 14 (33%) | 10 (50%) | 2 (29%) | 37 (14%) | 5 (56%) | 2 (100%) | 0 (0%) | 3 (100%) | 2 (10%) | 185 (32%) | 2 (40%) | 1,888 (43%) | 0 (0%) | 190 (32%) | 59 (41%) | 0 (0%) | 2 (67%) | 0 (0%) | 10 (77%) | 68 (40%) | 0 (0%) | 13 (54%) | 2 (50%) | 2 (20%) | 0 (0%) | 0 (0%) | 2 (100%) | 13 (36%) | 0 (0%) | 0 (0%) | 0 (0%) | 9 (53%) | 0 (0%) | 2 (8.7%) | 3 (60%) | 1 (100%) | 21 (62%) | 0 (0%) | 0 (0%) | 3 (50%) | 2 (100%) | 3 (100%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 38 (31%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (3.6%) | 7 (41%) | 2 (50%) | 5 (21%) | 30 (20%) | 0 (0%) | 2 (100%) | 0 (0%) | 1 (33%) | 2 (67%) | 14 (100%) | 16 (32%) | 0 (0%) | 24 (33%) | 4 (100%) | 2 (100%) | 13 (19%) | 0 (0%) | 1 (100%) | 0 (0%) | 3 (21%) | 31 (38%) | 2 (18%) | 4 (44%) | 3 (100%) | 2 (100%) | 354 (95%) | 1 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 10 (21%) | 0 (0%) | 9 (5.6%) | 2 (67%) | 9 (69%) | 217 (31%) | 47 (68%) | 2 (100%) | 10 (30%) | 7 (30%) | 0 (0%) | 0 (0%) | 4,609 (41%) | 2 (40%) | 2 (100%) | 4 (100%) | 17 (55%) | 2 (100%) | 3 (100%) | 4 (67%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 0 (0%) | 4 (100%) | 4 (100%) | 0 (0%) | 4 (80%) | 2 (100%) | 1 (100%) | 2 (50%) | 0 (0%) | 2 (100%) | 0 (0%) | 3 (100%) | 0 (0%) | 3 (100%) | 2 (100%) | 2 (100%) | 4 (67%) | 0 (0%) | 14 (31%) | 7 (100%) | 28 (17%) | 3 (100%) | 0 (0%) | 1 (100%) | 0 (0%) | 2 (22%) | 4 (67%) | 48 (8.4%) | 3 (1.6%) | 0 (0%) | 3 (100%) | 11 (26%) | 20 (5.5%) | 1 (100%) | 0 (0%) | 6 (24%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (26%) | 0 (0%) | 3 (100%) | 3 (100%) | 15 (33%) | 0 (0%) | 64 (39%) | 0 (0%) | 18 (72%) | 21 (31%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (38%) | 8 (36%) | 0 (0%) | 2 (50%) | 2 (100%) | 6 (55%) | 190 (55%) | 32 (76%) | 26 (33%) | 2 (2.8%) |
| Refused to Disclose | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (3.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (<0.1%) | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 20 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Transgender | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (22%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 7 (1.2%) | 0 (0%) | 18 (0.4%) | 0 (0%) | 3 (0.5%) | 3 (2.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 0 (0%) | 2 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 39 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (11%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (9.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.6%) | 0 (0%) | 0 (0%) | 0 (0%) |
| DISABILITY_STATUS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| No | 1 (33%) | 2 (100%) | 3 (75%) | 2 (100%) | 2 (100%) | 0 (0%) | 11 (61%) | 11 (100%) | 3 (50%) | 20 (53%) | 2 (50%) | 0 (0%) | 9 (53%) | 2 (67%) | 3 (100%) | 3 (100%) | 4 (100%) | 35 (53%) | 7 (54%) | 7 (78%) | 2 (100%) | 3 (100%) | 19 (95%) | 5 (100%) | 0 (NA%) | 1 (100%) | 87 (74%) | 7 (41%) | 27 (64%) | 9 (45%) | 5 (71%) | 204 (79%) | 7 (78%) | 2 (100%) | 2 (100%) | 0 (0%) | 12 (60%) | 375 (65%) | 4 (80%) | 2,928 (67%) | 6 (100%) | 437 (75%) | 92 (63%) | 2 (100%) | 1 (33%) | 2 (100%) | 9 (69%) | 80 (47%) | 0 (0%) | 17 (71%) | 2 (50%) | 8 (80%) | 6 (55%) | 0 (0%) | 0 (0%) | 22 (61%) | 1 (100%) | 5 (100%) | 2 (100%) | 9 (53%) | 7 (100%) | 16 (70%) | 0 (0%) | 1 (100%) | 22 (65%) | 4 (67%) | 9 (100%) | 3 (50%) | 0 (0%) | 0 (0%) | 2 (100%) | 1 (100%) | 0 (NA%) | 1 (100%) | 101 (81%) | 2 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 108 (76%) | 12 (71%) | 4 (100%) | 13 (54%) | 97 (62%) | 1 (100%) | 2 (100%) | 2 (100%) | 2 (67%) | 1 (33%) | 6 (43%) | 27 (54%) | 2 (100%) | 45 (62%) | 4 (100%) | 0 (0%) | 52 (75%) | 3 (100%) | 1 (100%) | 2 (50%) | 13 (93%) | 18 (22%) | 7 (64%) | 7 (78%) | 0 (0%) | 0 (0%) | 267 (72%) | 1 (100%) | 17 (100%) | 2 (100%) | 1 (33%) | 27 (56%) | 1 (100%) | 114 (72%) | 3 (100%) | 2 (15%) | 461 (66%) | 53 (77%) | 0 (0%) | 23 (70%) | 14 (61%) | 3 (60%) | 0 (0%) | 7,503 (68%) | 0 (0%) | 2 (100%) | 2 (50%) | 15 (48%) | 2 (100%) | 0 (0%) | 2 (33%) | 1 (50%) | 1 (100%) | 3 (100%) | 0 (0%) | 2 (100%) | 2 (100%) | 2 (50%) | 2 (50%) | 0 (0%) | 5 (100%) | 0 (0%) | 1 (100%) | 0 (0%) | 0 (0%) | 2 (100%) | 2 (50%) | 3 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (67%) | 0 (0%) | 28 (62%) | 3 (43%) | 123 (76%) | 3 (100%) | 3 (100%) | 1 (100%) | 1 (20%) | 9 (100%) | 4 (67%) | 418 (76%) | 152 (79%) | 1 (50%) | 3 (100%) | 28 (67%) | 257 (71%) | 1 (100%) | 2 (100%) | 21 (84%) | 0 (0%) | 5 (100%) | 0 (0%) | 4 (50%) | 11 (58%) | 0 (0%) | 0 (0%) | 0 (0%) | 26 (57%) | 2 (100%) | 95 (58%) | 9 (82%) | 18 (72%) | 39 (58%) | 3 (100%) | 3 (100%) | 23 (61%) | 4 (67%) | 3 (38%) | 10 (45%) | 4 (67%) | 3 (100%) | 0 (0%) | 5 (45%) | 229 (66%) | 28 (67%) | 40 (51%) | 52 (72%) |
| Not Collected | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (3.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 1 (0.8%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.8%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 21 (0.5%) | 0 (0%) | 1 (0.2%) | 1 (0.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (2.9%) | 0 (0%) | 2 (8.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (12%) | 0 (0%) | 2 (8.7%) | 0 (0%) | 0 (0%) | 2 (5.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (67%) | 0 (0%) | 2 (4.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (18%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (1.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (0.6%) | 2 (2.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 409 (3.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 2 (33%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 11 (2.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (0.8%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (5.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 0 (0%) | 9 (2.6%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Refused to Disclose | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (5.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.2%) | 0 (0%) | 10 (0.2%) | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 25 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (0.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Yes | 2 (67%) | 0 (0%) | 1 (25%) | 0 (0%) | 0 (0%) | 2 (100%) | 7 (39%) | 0 (0%) | 3 (50%) | 18 (47%) | 2 (50%) | 1 (100%) | 8 (47%) | 1 (33%) | 0 (0%) | 0 (0%) | 0 (0%) | 29 (44%) | 6 (46%) | 2 (22%) | 0 (0%) | 0 (0%) | 1 (5.0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 30 (25%) | 10 (59%) | 15 (36%) | 10 (50%) | 2 (29%) | 52 (20%) | 2 (22%) | 0 (0%) | 0 (0%) | 3 (100%) | 8 (40%) | 197 (34%) | 1 (20%) | 1,433 (33%) | 0 (0%) | 142 (24%) | 52 (36%) | 0 (0%) | 2 (67%) | 0 (0%) | 4 (31%) | 87 (51%) | 3 (100%) | 5 (21%) | 2 (50%) | 2 (20%) | 5 (45%) | 1 (100%) | 2 (100%) | 14 (39%) | 0 (0%) | 0 (0%) | 0 (0%) | 6 (35%) | 0 (0%) | 5 (22%) | 5 (100%) | 0 (0%) | 10 (29%) | 2 (33%) | 0 (0%) | 3 (50%) | 2 (100%) | 3 (100%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 23 (19%) | 0 (0%) | 2 (100%) | 2 (100%) | 2 (100%) | 35 (24%) | 5 (29%) | 0 (0%) | 11 (46%) | 59 (38%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (33%) | 0 (0%) | 8 (57%) | 20 (40%) | 0 (0%) | 28 (38%) | 0 (0%) | 2 (100%) | 17 (25%) | 0 (0%) | 0 (0%) | 2 (50%) | 1 (7.1%) | 63 (78%) | 2 (18%) | 2 (22%) | 3 (100%) | 2 (100%) | 102 (27%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (67%) | 21 (44%) | 0 (0%) | 44 (28%) | 0 (0%) | 11 (85%) | 235 (34%) | 14 (20%) | 2 (100%) | 10 (30%) | 9 (39%) | 2 (40%) | 3 (100%) | 3,170 (29%) | 5 (100%) | 0 (0%) | 2 (50%) | 16 (52%) | 0 (0%) | 3 (100%) | 4 (67%) | 1 (50%) | 0 (0%) | 0 (0%) | 2 (100%) | 0 (0%) | 0 (0%) | 2 (50%) | 2 (50%) | 2 (100%) | 0 (0%) | 2 (100%) | 0 (0%) | 4 (100%) | 2 (100%) | 0 (0%) | 2 (50%) | 0 (0%) | 2 (100%) | 3 (100%) | 2 (100%) | 0 (0%) | 0 (0%) | 2 (100%) | 17 (38%) | 4 (57%) | 39 (24%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (80%) | 0 (0%) | 2 (33%) | 119 (22%) | 40 (21%) | 1 (50%) | 0 (0%) | 14 (33%) | 103 (28%) | 0 (0%) | 0 (0%) | 4 (16%) | 2 (100%) | 0 (0%) | 3 (100%) | 4 (50%) | 8 (42%) | 3 (100%) | 3 (100%) | 3 (100%) | 20 (43%) | 0 (0%) | 68 (42%) | 2 (18%) | 7 (28%) | 28 (42%) | 0 (0%) | 0 (0%) | 13 (34%) | 2 (33%) | 5 (62%) | 12 (55%) | 2 (33%) | 0 (0%) | 0 (0%) | 6 (55%) | 108 (31%) | 14 (33%) | 38 (49%) | 20 (28%) |
| MILITARY_STATUS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Active | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (<0.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 14 (0.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| No | 3 (100%) | 2 (100%) | 4 (100%) | 2 (100%) | 2 (100%) | 2 (100%) | 15 (83%) | 8 (73%) | 6 (100%) | 8 (21%) | 2 (50%) | 1 (100%) | 17 (100%) | 3 (100%) | 3 (100%) | 3 (100%) | 4 (100%) | 57 (92%) | 13 (100%) | 9 (100%) | 2 (100%) | 2 (67%) | 18 (90%) | 5 (100%) | 0 (NA%) | 0 (0%) | 114 (97%) | 12 (80%) | 40 (95%) | 17 (89%) | 7 (100%) | 243 (95%) | 7 (100%) | 0 (0%) | 2 (100%) | 0 (0%) | 20 (100%) | 535 (94%) | 5 (100%) | 4,107 (94%) | 6 (100%) | 559 (96%) | 143 (99%) | 2 (100%) | 3 (100%) | 2 (100%) | 13 (100%) | 159 (95%) | 3 (100%) | 22 (92%) | 4 (100%) | 8 (80%) | 11 (100%) | 1 (100%) | 2 (100%) | 32 (89%) | 1 (100%) | 5 (100%) | 2 (100%) | 11 (65%) | 7 (100%) | 20 (87%) | 5 (100%) | 1 (100%) | 29 (85%) | 4 (100%) | 7 (78%) | 3 (50%) | 0 (0%) | 3 (100%) | 2 (100%) | 1 (100%) | 0 (NA%) | 1 (100%) | 123 (99%) | 2 (100%) | 0 (NA%) | 2 (100%) | 0 (NA%) | 137 (96%) | 17 (100%) | 4 (100%) | 24 (100%) | 154 (99%) | 1 (100%) | 2 (100%) | 2 (100%) | 3 (100%) | 1 (33%) | 14 (100%) | 42 (86%) | 2 (100%) | 66 (90%) | 4 (100%) | 2 (100%) | 60 (87%) | 0 (0%) | 1 (100%) | 4 (100%) | 14 (100%) | 79 (98%) | 9 (82%) | 9 (100%) | 3 (100%) | 2 (100%) | 340 (93%) | 1 (100%) | 14 (82%) | 2 (100%) | 3 (100%) | 45 (94%) | 1 (100%) | 150 (95%) | 0 (0%) | 13 (100%) | 641 (93%) | 64 (93%) | 2 (100%) | 33 (100%) | 21 (91%) | 5 (100%) | 3 (100%) | 10,303 (94%) | 5 (100%) | 2 (100%) | 4 (100%) | 26 (84%) | 2 (100%) | 3 (100%) | 4 (100%) | 2 (100%) | 1 (100%) | 3 (100%) | 2 (100%) | 2 (100%) | 2 (100%) | 4 (100%) | 4 (100%) | 2 (100%) | 5 (100%) | 2 (100%) | 0 (0%) | 0 (0%) | 2 (100%) | 2 (100%) | 4 (100%) | 3 (100%) | 2 (100%) | 3 (100%) | 2 (100%) | 0 (0%) | 4 (67%) | 2 (100%) | 40 (89%) | 7 (100%) | 159 (98%) | 3 (100%) | 3 (100%) | 1 (100%) | 5 (100%) | 6 (67%) | 4 (67%) | 517 (95%) | 192 (100%) | 2 (100%) | 3 (100%) | 33 (79%) | 341 (97%) | 1 (100%) | 2 (100%) | 25 (100%) | 2 (100%) | 5 (100%) | 3 (100%) | 8 (100%) | 19 (100%) | 3 (100%) | 0 (0%) | 3 (100%) | 43 (93%) | 2 (100%) | 155 (97%) | 11 (100%) | 23 (92%) | 59 (88%) | 3 (100%) | 3 (100%) | 37 (97%) | 5 (83%) | 0 (0%) | 16 (80%) | 4 (67%) | 4 (100%) | 0 (0%) | 11 (100%) | 315 (92%) | 0 (0%) | 78 (100%) | 69 (97%) |
| Not Collected | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (50%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.8%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 9 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (4.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (18%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (0.8%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 44 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (0.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (4.8%) | 5 (1.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.6%) | 0 (0%) | 0 (0%) | 2 (10%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (1.5%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Refused to Disclose | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 8 (0.2%) | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (22%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 11 (0.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (0.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Veteran | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (17%) | 3 (27%) | 0 (0%) | 31 (79%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (8.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (33%) | 2 (10%) | 0 (0%) | 0 (NA%) | 1 (100%) | 4 (3.4%) | 3 (20%) | 2 (4.8%) | 2 (11%) | 0 (0%) | 10 (3.9%) | 0 (0%) | 2 (100%) | 0 (0%) | 3 (100%) | 0 (0%) | 32 (5.6%) | 0 (0%) | 245 (5.6%) | 0 (0%) | 22 (3.8%) | 1 (0.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 7 (4.2%) | 0 (0%) | 2 (8.3%) | 0 (0%) | 2 (20%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (11%) | 0 (0%) | 0 (0%) | 0 (0%) | 6 (35%) | 0 (0%) | 3 (13%) | 0 (0%) | 0 (0%) | 5 (15%) | 0 (0%) | 0 (0%) | 3 (50%) | 2 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 1 (0.8%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (NA%) | 6 (4.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (1.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (67%) | 0 (0%) | 5 (10%) | 0 (0%) | 7 (9.6%) | 0 (0%) | 0 (0%) | 9 (13%) | 3 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (2.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 22 (6.0%) | 0 (0%) | 3 (18%) | 0 (0%) | 0 (0%) | 3 (6.2%) | 0 (0%) | 8 (5.1%) | 3 (100%) | 0 (0%) | 49 (7.1%) | 5 (7.2%) | 0 (0%) | 0 (0%) | 2 (8.7%) | 0 (0%) | 0 (0%) | 602 (5.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (16%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) | 4 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 2 (33%) | 0 (0%) | 5 (11%) | 0 (0%) | 3 (1.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (33%) | 2 (33%) | 16 (2.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 7 (17%) | 7 (2.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (100%) | 0 (0%) | 3 (6.5%) | 0 (0%) | 5 (3.1%) | 0 (0%) | 2 (8.0%) | 8 (12%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (17%) | 8 (100%) | 2 (10%) | 2 (33%) | 0 (0%) | 2 (100%) | 0 (0%) | 22 (6.4%) | 42 (100%) | 0 (0%) | 2 (2.8%) |
| AGE | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 17 - younger | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (0.7%) | 0 (0%) | 7 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (20%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (2.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (1.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (0.6%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 35 (0.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.2%) | 5 (2.6%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (20%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| 18 - 59 | 3 (100%) | 2 (100%) | 4 (100%) | 2 (100%) | 2 (100%) | 2 (100%) | 15 (83%) | 11 (100%) | 0 (0%) | 37 (97%) | 2 (50%) | 1 (100%) | 16 (94%) | 3 (100%) | 3 (100%) | 3 (100%) | 4 (100%) | 64 (97%) | 11 (85%) | 9 (100%) | 2 (100%) | 2 (67%) | 20 (100%) | 5 (100%) | 0 (NA%) | 1 (100%) | 111 (94%) | 12 (71%) | 42 (100%) | 19 (95%) | 7 (100%) | 249 (98%) | 9 (100%) | 2 (100%) | 2 (100%) | 0 (0%) | 20 (100%) | 529 (92%) | 5 (100%) | 4,031 (92%) | 6 (100%) | 536 (93%) | 139 (96%) | 2 (100%) | 1 (33%) | 2 (100%) | 13 (100%) | 149 (87%) | 3 (100%) | 20 (83%) | 2 (50%) | 8 (80%) | 11 (100%) | 1 (100%) | 2 (100%) | 33 (92%) | 1 (100%) | 5 (100%) | 2 (100%) | 15 (88%) | 7 (100%) | 23 (100%) | 2 (40%) | 1 (100%) | 32 (94%) | 6 (100%) | 9 (100%) | 6 (100%) | 0 (0%) | 3 (100%) | 2 (100%) | 1 (100%) | 0 (NA%) | 1 (100%) | 118 (95%) | 2 (100%) | 2 (100%) | 2 (100%) | 2 (100%) | 133 (93%) | 17 (100%) | 2 (50%) | 22 (85%) | 144 (92%) | 0 (0%) | 2 (100%) | 2 (100%) | 3 (100%) | 1 (33%) | 10 (71%) | 36 (72%) | 2 (100%) | 69 (95%) | 4 (100%) | 2 (100%) | 69 (100%) | 3 (100%) | 1 (100%) | 4 (100%) | 14 (100%) | 51 (63%) | 9 (82%) | 7 (78%) | 0 (0%) | 2 (100%) | 347 (93%) | 0 (0%) | 17 (100%) | 2 (100%) | 3 (100%) | 48 (100%) | 1 (100%) | 154 (97%) | 3 (100%) | 11 (85%) | 625 (89%) | 64 (93%) | 0 (0%) | 33 (100%) | 22 (96%) | 0 (0%) | 3 (100%) | 10,153 (91%) | 5 (100%) | 2 (100%) | 4 (100%) | 26 (84%) | 2 (100%) | 3 (100%) | 4 (67%) | 2 (100%) | 1 (100%) | 0 (0%) | 0 (0%) | 2 (100%) | 2 (100%) | 2 (50%) | 4 (100%) | 2 (100%) | 5 (100%) | 2 (100%) | 0 (0%) | 3 (75%) | 2 (100%) | 0 (0%) | 4 (100%) | 3 (100%) | 2 (100%) | 0 (0%) | 2 (100%) | 0 (0%) | 4 (67%) | 0 (0%) | 43 (96%) | 7 (100%) | 159 (98%) | 3 (100%) | 3 (100%) | 1 (100%) | 5 (100%) | 9 (100%) | 5 (83%) | 525 (95%) | 184 (96%) | 2 (100%) | 3 (100%) | 41 (98%) | 346 (95%) | 1 (100%) | 2 (100%) | 23 (92%) | 2 (100%) | 5 (100%) | 3 (100%) | 6 (75%) | 15 (79%) | 3 (100%) | 0 (0%) | 3 (100%) | 46 (96%) | 2 (100%) | 151 (93%) | 11 (100%) | 18 (72%) | 63 (94%) | 3 (100%) | 3 (100%) | 37 (97%) | 6 (100%) | 8 (100%) | 18 (82%) | 4 (67%) | 3 (100%) | 0 (0%) | 11 (100%) | 320 (93%) | 34 (81%) | 73 (94%) | 64 (90%) |
| 60 - 64 | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.6%) | 0 (0%) | 0 (0%) | 1 (5.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (3.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 2 (1.7%) | 3 (18%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 22 (3.8%) | 0 (0%) | 191 (4.4%) | 0 (0%) | 18 (3.1%) | 3 (2.1%) | 0 (0%) | 2 (67%) | 0 (0%) | 0 (0%) | 13 (7.6%) | 0 (0%) | 2 (8.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (8.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (60%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 4 (3.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (2.8%) | 0 (0%) | 0 (0%) | 2 (7.7%) | 8 (5.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (29%) | 7 (14%) | 0 (0%) | 4 (5.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 15 (19%) | 0 (0%) | 2 (22%) | 0 (0%) | 0 (0%) | 24 (6.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (1.3%) | 0 (0%) | 2 (15%) | 35 (5.0%) | 3 (4.3%) | 2 (100%) | 0 (0%) | 0 (0%) | 2 (40%) | 0 (0%) | 530 (4.8%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (6.5%) | 0 (0%) | 0 (0%) | 2 (33%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (67%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 2 (4.4%) | 0 (0%) | 1 (0.6%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (17%) | 9 (1.6%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.4%) | 14 (3.8%) | 0 (0%) | 0 (0%) | 2 (8.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (25%) | 4 (21%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 6 (3.7%) | 0 (0%) | 2 (8.0%) | 4 (6.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (9.1%) | 2 (33%) | 0 (0%) | 0 (0%) | 0 (0%) | 8 (2.3%) | 5 (12%) | 3 (3.8%) | 7 (9.9%) |
| 65 - older | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (17%) | 0 (0%) | 6 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (15%) | 0 (0%) | 0 (0%) | 1 (33%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 5 (4.2%) | 2 (12%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.8%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (100%) | 0 (0%) | 18 (3.1%) | 0 (0%) | 141 (3.2%) | 0 (0%) | 16 (2.8%) | 3 (2.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 8 (4.7%) | 0 (0%) | 0 (0%) | 2 (50%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 2 (1.6%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (2.1%) | 0 (0%) | 2 (50%) | 2 (7.7%) | 2 (1.3%) | 1 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (8.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 15 (19%) | 0 (0%) | 0 (0%) | 3 (100%) | 0 (0%) | 3 (0.8%) | 1 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (1.3%) | 0 (0%) | 0 (0%) | 35 (5.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (4.3%) | 3 (60%) | 0 (0%) | 317 (2.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (9.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 0 (0%) | 0 (0%) | 2 (50%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (25%) | 0 (0%) | 2 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (33%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 7 (1.3%) | 3 (1.6%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (0.8%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (100%) | 0 (0%) | 2 (4.2%) | 0 (0%) | 6 (3.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (9.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 11 (3.2%) | 3 (7.1%) | 2 (2.6%) | 0 (0%) |
| Not Collected | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (50%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 9 (0.2%) | 0 (0%) | 4 (0.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (1.2%) | 0 (0%) | 2 (8.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (12%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (5.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (67%) | 0 (0%) | 2 (4.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (18%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (2.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 46 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 2 (33%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 9 (1.6%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.6%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 0 (0%) | 4 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Refused to Disclose | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (5.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 11 (0.3%) | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (NA%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 19 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| NEED | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Housing | 1 (33%) | 1 (50%) | 0 (0%) | 1 (50%) | 0 (0%) | 0 (0%) | 7 (39%) | 4 (36%) | 2 (33%) | 21 (54%) | 3 (50%) | 1 (100%) | 4 (24%) | 3 (100%) | 1 (33%) | 1 (33%) | 1 (25%) | 21 (30%) | 4 (31%) | 2 (22%) | 0 (0%) | 2 (67%) | 5 (25%) | 2 (40%) | 1 (100%) | 1 (100%) | 34 (28%) | 4 (24%) | 13 (30%) | 5 (25%) | 2 (29%) | 160 (61%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (33%) | 6 (30%) | 287 (49%) | 4 (80%) | 3,994 (90%) | 3 (50%) | 365 (52%) | 63 (43%) | 1 (50%) | 2 (67%) | 1 (50%) | 4 (31%) | 50 (29%) | 1 (33%) | 6 (25%) | 1 (25%) | 5 (50%) | 3 (27%) | 1 (100%) | 1 (50%) | 6 (17%) | 2 (100%) | 3 (60%) | 1 (50%) | 4 (24%) | 5 (71%) | 6 (26%) | 2 (40%) | 0 (0%) | 10 (27%) | 0 (0%) | 3 (33%) | 2 (33%) | 1 (50%) | 1 (33%) | 2 (100%) | 2 (67%) | 1 (50%) | 1 (100%) | 62 (50%) | 1 (50%) | 0 (0%) | 1 (50%) | 0 (0%) | 74 (51%) | 6 (35%) | 2 (50%) | 5 (19%) | 84 (54%) | 0 (0%) | 0 (0%) | 1 (50%) | 1 (33%) | 1 (33%) | 3 (21%) | 14 (28%) | 0 (0%) | 19 (25%) | 0 (0%) | 1 (50%) | 17 (25%) | 1 (33%) | 1 (100%) | 2 (50%) | 6 (43%) | 50 (59%) | 3 (27%) | 1 (11%) | 1 (33%) | 0 (0%) | 296 (77%) | 0 (0%) | 4 (24%) | 1 (50%) | 2 (67%) | 12 (24%) | 0 (0%) | 61 (38%) | 2 (67%) | 4 (27%) | 406 (57%) | 18 (25%) | 1 (50%) | 10 (30%) | 21 (91%) | 1 (20%) | 1 (33%) | 2,074 (18%) | 1 (20%) | 0 (0%) | 2 (50%) | 7 (23%) | 0 (0%) | 1 (33%) | 1 (17%) | 0 (0%) | 0 (0%) | 1 (33%) | 1 (50%) | 1 (50%) | 1 (50%) | 1 (25%) | 1 (25%) | 0 (0%) | 0 (0%) | 1 (50%) | 1 (100%) | 3 (75%) | 1 (50%) | 1 (50%) | 2 (50%) | 1 (33%) | 1 (50%) | 2 (40%) | 1 (50%) | 1 (50%) | 1 (17%) | 1 (50%) | 10 (22%) | 2 (29%) | 90 (56%) | 1 (33%) | 1 (33%) | 1 (100%) | 3 (60%) | 4 (44%) | 4 (67%) | 397 (67%) | 93 (48%) | 2 (100%) | 1 (33%) | 28 (67%) | 276 (75%) | 1 (100%) | 1 (50%) | 12 (48%) | 1 (50%) | 2 (40%) | 1 (33%) | 0 (0%) | 4 (19%) | 1 (33%) | 1 (33%) | 1 (33%) | 11 (23%) | 1 (50%) | 88 (54%) | 6 (55%) | 12 (48%) | 18 (27%) | 1 (33%) | 1 (33%) | 23 (51%) | 5 (83%) | 3 (38%) | 5 (23%) | 2 (33%) | 0 (0%) | 1 (50%) | 2 (18%) | 190 (53%) | 8 (19%) | 17 (22%) | 42 (58%) |
| Information Services | 2 (67%) | 1 (50%) | 3 (75%) | 0 (0%) | 1 (50%) | 1 (50%) | 3 (17%) | 2 (18%) | 2 (33%) | 15 (38%) | 0 (0%) | 0 (0%) | 5 (29%) | 0 (0%) | 1 (33%) | 1 (33%) | 1 (25%) | 13 (19%) | 3 (23%) | 3 (33%) | 1 (50%) | 1 (33%) | 6 (30%) | 1 (20%) | 0 (0%) | 0 (0%) | 25 (21%) | 5 (29%) | 10 (23%) | 6 (30%) | 2 (29%) | 87 (33%) | 4 (44%) | 1 (50%) | 1 (50%) | 1 (33%) | 6 (30%) | 216 (37%) | 1 (20%) | 272 (6.1%) | 3 (50%) | 177 (25%) | 39 (27%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (31%) | 41 (24%) | 1 (33%) | 7 (29%) | 1 (25%) | 4 (40%) | 4 (36%) | 0 (0%) | 0 (0%) | 10 (28%) | 0 (0%) | 2 (40%) | 1 (50%) | 5 (29%) | 1 (14%) | 6 (26%) | 1 (20%) | 0 (0%) | 7 (19%) | 3 (50%) | 4 (44%) | 2 (33%) | 0 (0%) | 1 (33%) | 0 (0%) | 1 (33%) | 1 (50%) | 0 (0%) | 52 (42%) | 1 (50%) | 1 (50%) | 1 (50%) | 1 (50%) | 58 (40%) | 3 (18%) | 0 (0%) | 9 (35%) | 58 (37%) | 1 (100%) | 1 (50%) | 1 (50%) | 0 (0%) | 0 (0%) | 4 (29%) | 13 (26%) | 1 (50%) | 22 (29%) | 2 (50%) | 0 (0%) | 22 (32%) | 1 (33%) | 0 (0%) | 0 (0%) | 4 (29%) | 29 (34%) | 3 (27%) | 3 (33%) | 1 (33%) | 1 (50%) | 62 (16%) | 0 (0%) | 6 (35%) | 0 (0%) | 0 (0%) | 16 (32%) | 0 (0%) | 15 (9.3%) | 0 (0%) | 4 (27%) | 232 (33%) | 20 (28%) | 0 (0%) | 6 (18%) | 0 (0%) | 2 (40%) | 1 (33%) | 8,821 (78%) | 2 (40%) | 1 (50%) | 2 (50%) | 10 (32%) | 1 (50%) | 1 (33%) | 2 (33%) | 2 (100%) | 0 (0%) | 1 (33%) | 1 (50%) | 0 (0%) | 0 (0%) | 1 (25%) | 1 (25%) | 1 (50%) | 2 (40%) | 0 (0%) | 0 (0%) | 1 (25%) | 1 (50%) | 1 (50%) | 2 (50%) | 1 (33%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (33%) | 1 (50%) | 17 (38%) | 2 (29%) | 53 (33%) | 1 (33%) | 1 (33%) | 0 (0%) | 0 (0%) | 1 (11%) | 2 (33%) | 92 (15%) | 84 (43%) | 0 (0%) | 1 (33%) | 13 (31%) | 69 (19%) | 0 (0%) | 1 (50%) | 12 (48%) | 0 (0%) | 1 (20%) | 1 (33%) | 4 (50%) | 7 (33%) | 1 (33%) | 1 (33%) | 1 (33%) | 17 (35%) | 0 (0%) | 65 (40%) | 3 (27%) | 10 (40%) | 20 (30%) | 0 (0%) | 1 (33%) | 6 (13%) | 0 (0%) | 2 (25%) | 6 (27%) | 3 (50%) | 4 (100%) | 0 (0%) | 3 (27%) | 105 (29%) | 19 (45%) | 26 (33%) | 23 (32%) |
| Others | 0 (0%) | 0 (0%) | 1 (25%) | 1 (50%) | 1 (50%) | 1 (50%) | 8 (44%) | 5 (45%) | 2 (33%) | 3 (7.7%) | 3 (50%) | 0 (0%) | 8 (47%) | 0 (0%) | 1 (33%) | 1 (33%) | 2 (50%) | 35 (51%) | 6 (46%) | 4 (44%) | 1 (50%) | 0 (0%) | 9 (45%) | 2 (40%) | 0 (0%) | 0 (0%) | 61 (51%) | 8 (47%) | 21 (48%) | 9 (45%) | 3 (43%) | 14 (5.4%) | 5 (56%) | 1 (50%) | 1 (50%) | 1 (33%) | 8 (40%) | 77 (13%) | 0 (0%) | 168 (3.8%) | 0 (0%) | 159 (23%) | 43 (30%) | 1 (50%) | 1 (33%) | 1 (50%) | 5 (38%) | 83 (48%) | 1 (33%) | 11 (46%) | 2 (50%) | 1 (10%) | 4 (36%) | 0 (0%) | 1 (50%) | 20 (56%) | 0 (0%) | 0 (0%) | 0 (0%) | 8 (47%) | 1 (14%) | 11 (48%) | 2 (40%) | 1 (100%) | 20 (54%) | 3 (50%) | 2 (22%) | 2 (33%) | 1 (50%) | 1 (33%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 11 (8.8%) | 0 (0%) | 1 (50%) | 0 (0%) | 1 (50%) | 13 (9.0%) | 8 (47%) | 2 (50%) | 12 (46%) | 14 (9.0%) | 0 (0%) | 1 (50%) | 0 (0%) | 2 (67%) | 2 (67%) | 7 (50%) | 23 (46%) | 1 (50%) | 34 (45%) | 2 (50%) | 1 (50%) | 30 (43%) | 1 (33%) | 0 (0%) | 2 (50%) | 4 (29%) | 6 (7.1%) | 5 (45%) | 5 (56%) | 1 (33%) | 1 (50%) | 26 (6.8%) | 1 (100%) | 7 (41%) | 1 (50%) | 1 (33%) | 22 (44%) | 1 (100%) | 85 (53%) | 1 (33%) | 7 (47%) | 73 (10%) | 34 (47%) | 1 (50%) | 17 (52%) | 2 (8.7%) | 2 (40%) | 1 (33%) | 448 (3.9%) | 2 (40%) | 1 (50%) | 0 (0%) | 14 (45%) | 1 (50%) | 1 (33%) | 3 (50%) | 0 (0%) | 1 (100%) | 1 (33%) | 0 (0%) | 1 (50%) | 1 (50%) | 2 (50%) | 2 (50%) | 1 (50%) | 3 (60%) | 1 (50%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (33%) | 1 (50%) | 3 (60%) | 1 (50%) | 1 (50%) | 3 (50%) | 0 (0%) | 18 (40%) | 3 (43%) | 19 (12%) | 1 (33%) | 1 (33%) | 0 (0%) | 2 (40%) | 4 (44%) | 0 (0%) | 105 (18%) | 17 (8.8%) | 0 (0%) | 1 (33%) | 1 (2.4%) | 25 (6.8%) | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (50%) | 2 (40%) | 1 (33%) | 4 (50%) | 10 (48%) | 1 (33%) | 1 (33%) | 1 (33%) | 20 (42%) | 1 (50%) | 10 (6.1%) | 2 (18%) | 3 (12%) | 29 (43%) | 2 (67%) | 1 (33%) | 16 (36%) | 1 (17%) | 3 (38%) | 11 (50%) | 1 (17%) | 0 (0%) | 1 (50%) | 6 (55%) | 64 (18%) | 15 (36%) | 35 (45%) | 7 (9.7%) |
|
1
n (%)
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#write.csv(q2,"../Data/Output/Q2.csv",row.names = F)